To ensure a fair comparison, we also compare our common object localization unit
to the Greedy Tree by exactly following the original experimental protocol
in [1]}.
Their algorithm utilizes a split of the COCO 2017
dataset with 63 base classes for training and 17 held-out novel classes for
testing the algorithm.
The trained model is also tested on a subset of the ILSVRC
2013 detection dataset with 148 novel classes that have no overlap with the base
classes.
In Greedy Tree, a Faster-RCNN with ResNet50 [2]} backbone
is first trained on the base classes and used to extract features from all the
images.
To allow a fair comparison, we use the same feature set provided by the
authors.
To mimic common object localization during training, we sample
tasks with \(N=1\)  and \(K=8\)  for training.
